The present invention relates generally to field of medical data processing, acquisition and analysis. More particularly, the invention relates to techniques for drawing upon a wide range of available medical data for informing decisions related to diagnosis, treatment, further data processing, acquisition and analysis.
In the medical field many different tools are available for learning about and treating patient conditions. Traditionally, physicians would physically examine patients and draw upon a vast array of personal knowledge gleaned from years of study to identify problems and conditions experienced by patients, and to determine appropriate treatments. Sources of support information traditionally included other practitioners, reference books and manuals, relatively straightforward examination results and analyses, and so forth. Over the past decades, and particularly in recent years, a wide array of further reference materials have become available to the practitioner that greatly expand the resources available and enhance and improve patient care.
Among the diagnostic resources currently available to physicians and other caretakers are databases of information as well as sources which can be prescribed and controlled. The databases, are somewhat to conventional reference libraries, are know available from many sources and provide physicians with detailed information on possible disease states, information on how to recognize such states, and treatment of the states within seconds. Similar reference materials are, of course, available that identify such considerations as drug interactions, predispositions for disease and medical events, and so forth. Certain of these reference materials are available at no cost to care providers, while other are typically associated with a subscription or community membership.
Specific data acquisition techniques are also known that can be prescribed and controlled to explore potential physical conditions and medical events, and to pinpoint sources of potential medical problems. Traditional prescribable data sources included simple blood tests, urine tests, manually recorded results of physical examinations, and the like. Over recent decades, more sophisticated techniques have been developed that include various types of electrical data acquisition which detect and record the operation of systems of the body and, to some extent, the response of such systems to situations and stimuli. Even more sophisticated systems have been developed that provide images of the body, including internal features which could only be viewed and analyzed through surgical intervention before their development, and which permit viewing and analysis of other features and functions which could not have been seen in any other manner. All of these techniques have added to the vast array of resources available to physicians, and have greatly improved the quality of medical care.
Despite the dramatic increase and improvement in the sources of medical-related information, the prescription and analysis of tests and data, and the diagnosis and treatment of medical events still relies to a great degree upon the expertise of trained care providers. Input and judgment offered by human experience will not and should not be replaced in such situations. However, further improvements and integration of the sources of medical information are needed. While attempts have been made at allowing informed diagnosis and analysis in a somewhat automated fashion, these attempts have not even approached the level of integration and correlation which would be most useful in speedy and efficient patient care.
An increasing number of applications are being developed for automated analysis of medical-related data. Techniques have been developed, for example, in the medical imaging context for segmenting pathologies, identifying such features and classifying the features for possible diagnosis and treatment. However, limited integration of such programs has been provided in the past, and due to the very limited integration of data resources, little or no activity has focused on enhancing the performance of such algorithms by novel learning techniques. Such algorithms are typically refined by laborious feature recognition and reprogramming by teams of programmers and technicians.
There is a need, therefore, for an improved technique that would permit refinement of computer-assisted data analysis algorithms in the medical context. There is a particular need for techniques which can operate on existing and future algorithms to enhance their performance at all levels of functionality to approach and even surpass the capabilities of human readers of data.